11-2Aprogrammaticapproachtobuildcellularresponsemechanismsfromtime-dependentmulti-omicbig-data

发布时间 :2016-10-28  阅读次数 :1231

报告题目:A programmatic approach to build cellular response mechanisms from time-dependent multi-omic big-data

报  告 人:Dr. Carlos F. Lopez

Assistant Professor, Department of Cancer Biology, Vanderbilt University School of Medicine

报告时间:11月2日 10:00-11:30

报告地点:闵行校区生物药学楼2-116

联  系 人:王卓   This e-mail address is being protected from spambots. You need JavaScript enabled to view it.

 

Abstract:

Cellular processes comprise a complex network of biochemical interactions that give rise to cell-decisions and responses to perturbations. Although drug-based perturbations are standard in medical treatment, off-target effects often emerge as roadblocks that can lead cells to unwanted outcomes. To date, a mechanistic understanding of the cellular processes that are triggered upon perturbations have been considered in isolation, typically by studying a well-understood pathway or phenotype. Here we present a set of Python-based tools developed to capture the complexity of cellular signaling processes, explain signal execution, and predict outcomes in complex systems. The analysis utilizes time series proteomics, metabolomics, and transcriptomics, in combination with bioinformatics databases, to present a mechanistic description of the early response of cells to drug treatment. We present our results that infer the mechanism of A549 lung cancer cell in response to Cisplatin and HL60 cancer cell response to Bendamustine. We further explore the caveats associated with de novo mechanism inference and showcase how multiple signaling pathway databases can be leveraged to build truly large mechanisms. We finally demonstrate how the “hairball” of interactions that emerges from molecular interactions can be simplified to focus on the key mechanisms of cellular signaling that can drive hypotheses for experimental validation.

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